1. Field of the Invention
The present invention relates to a method and device for the calibration and equalization of the reception chains of an antenna processing system on board a satellite for example.
It can be used in a system of antenna processing on board a geostationary satellite for the calibration and equalization of reception chains. It is also used to verify the efficient functioning of these operations of calibration and equalization, which subsequently make it possible to diagnose malfunctions in the reception channels. The entire system can also be implemented from the ground, in order to locate and reject disturbances.
The invention can be applied for example in the field of space telecommunications.
FIG. 1 shows an exemplary goniometry or anti-interference system on board a satellite. It consists of a network of Q elementary sensors C1, . . . CQ, a set of active RF chains placed downstream from the sensors, possibly a device 3 for the formation of sub-arrays whose function is to reduce the number of chains to be digitized, a set 4 of reception chains integrating for example the digitization of the signals and a digitized data-processing device 5, distributed between onboard processing 5b and ground processing 5s. 
The processing algorithms used are generally based on the exploitation of the spatial consistency of the source between the different sensors of the network. This spatial consistency is also the basis of the techniques used for the rejection of interference sources by spatial filtering of the observations. This spatial consistency must be maintained at output of the reception and digitization chains to ensure the efficient functioning of the system.
In any operational system, the reception chains located downstream from the different sensors are rarely identical. To preserve the spatial consistency of a source it is necessary, at output of the chains, to provide precompensation for the differential responses of the reception chains in phase, amplitude and group time. This must be done throughout the reception band. FIG. 2 summarizes the steps used to obtain this precompensation. It is done by the filtering (Hk(z) filter) of the output of the reception chains and the associated operation is called chain equalization.
The operation of equalization is renewed as frequently as the drifts introduced at the reception chains by the parameters of influence go beyond a critical threshold. It subsequently becomes necessary to update the compensations, namely the equalization filters Hk(z). The parameters of influence identified are, for example, temperature, the ageing of the components, radiation and the adjustment of the gain of the reception chains.
Various methods described for the equalization of the reception chains necessitate, explicitly or implicitly, the learning of the inter-chain differential transfer functions Tk(f). This learning function is called calibration from which the equalization is deduced.
2. Description of the Prior Art
The prior art describes various techniques of calibration-equalization which may be gathered under two groups. These two groups can be differentiated especially by the capacity to adapt or not adapt to the presence of sensor observations during the calibration and equalization phase. Techniques that adapt to the presence of the observations have a low-power calibration signal added to them. However, for techniques that do not adapt to the presence of the observations, the calibration signal is injected in place of the observations.
For the group that does not adapt to the presence of the observed signals, two further sub-groups of processing are distinguished. The processing operations of the first group implement the calibration and equalization in an uncoupled way while the processing operations of the second group couple the calibration and equalization.
The second approach uses calibration and equalization in a coupled way, in seeking to obtain a situation where the outputs of the equalized chains show the greatest possible resemblance to the outputs of a chain known as a reference chain, using a criterion of minimization of the mean standard deviation for example.
The uncoupled approach implements calibration and equalization in a totally uncoupled way. In this case, the calibration, whose aim is to estimate the differential responses of the reception chains, is through the injection, into the input of these chains, of a calibration signal that may correspond to a sine wave whose frequency varies sequentially on the entire reception band. The measurements made at output of the chains makes it possible to estimate differential errors between chains and build equalizing filters at a second stage.
Before introducing the object of the present invention, a few points may be recalled on prior art methods of equalization.
A. Signals at Output of the Sensors
It is assumed that the array with N sensors (corresponding to radiating elements or preformed sub-arrays) of an antenna processing system receives a noise-ridden mixture of P (P≦N) narrow band (NB) sources. According to these assumptions, the vector v(t) of the complex envelopes of the signals at output of the sensors can be written, at a point in time t as follows:
                              v          ⁡                      (            t            )                          =                                            ∑                              p                =                1                            P                        ⁢                                                  ⁢                                                            m                  p                                ⁡                                  (                  t                  )                                            ⁢                              a                p                                              +                                    b              ⁡                              (                t                )                                      ⁢                                                  ⁢                          Δ              =                        ⁢                          Am              ⁡                              (                t                )                                              +                      b            ⁡                          (              t              )                                                          (        1        )            where b(t) is the noise vector, mp(t) and ap respectively correspond to the complex vector and to the direction vector of the source p, m(t) is the vector whose components are the values mp(t) and A is the matrix (N×P) whose columns are the vectors ap.
For any unspecified sensors, the component n of the direction vector ap is given byapn=fn(kp, ηp)exp(−j kp rn)  (2)where kp and ηp are respectively the wave vector and the polarization parameters of the source p, rn is the position vector of the sensor n and fn(kp, ηp) is the complex response of the sensor n in the direction of the wave vector kp for the polarization ηp.
The techniques of goniometry and certain antenna filtering or anti-interference techniques make implicit or explicit use of the models given by the relationships (1) and (2). However, the model (1) is not directly observable and only the model (described in the following paragraph B) of signals at output of the digitization chains is observable.
B. Signals at Output of the Digitization Chains
The signals at output of the sensors travel through the reception-digitization chains (with frequency responses Tn(f)) before they are observed. In a real system, these chains which, it is wished, should resemble each other to the greatest possible extent, generally remain substantially different and, furthermore, fluctuate for example because of temperature or ageing.
If Tn(t) denotes the pulse response of the digitization chain, n the component n of the observation vector, x(t), at output of the digitization chains is written as follows:
                                          x            n                    ⁡                      (            t            )                          =                                            ∑                              p                =                1                            P                        ⁢                                                            T                  n                                ⁡                                  (                  t                  )                                            *                                                m                  p                                ⁡                                  (                  t                  )                                            ⁢                              a                pn                                              +                                                    T                n                            ⁡                              (                t                )                                      *                                          b                n                            ⁡                              (                t                )                                                                        (        3        )            where bn(t) is the component n of the vector b(t). From the expression (3), we deduce that of the vector, x(t), given by
                              x          ⁡                      (            t            )                          =                                            ∑                              p                =                1                            P                        ⁢                                          T                ⁡                                  (                  t                  )                                            *                              a                p                            ⁢                                                m                  p                                ⁡                                  (                  t                  )                                                              +                                    T              ⁡                              (                t                )                                      *                          b              ⁡                              (                t                )                                      ⁢                          Δ              =                        ⁢                          T              ⁡                              (                t                )                                      *                          Am              ⁡                              (                t                )                                              +                                    T              ⁡                              (                t                )                                      *                          b              ⁡                              (                t                )                                                                        (        4        )            where T(t) is a diagonal matrix (N×N) whose diagonal terms are the quantities Tn(t). In the frequency domain, the model (4) is written as follows:
                              x          ⁡                      (            f            )                          =                                            ∑                              p                =                1                            P                        ⁢                                                            m                  p                                ⁡                                  (                  f                  )                                            ⁢                              T                ⁡                                  (                  f                  )                                            ⁢                              a                p                                              +                                    T              ⁡                              (                f                )                                      ⁢                          b              ⁡                              (                f                )                                      ⁢                          Δ              =                        ⁢                          T              ⁡                              (                f                )                                      ⁢                          Am              ⁡                              (                f                )                                              +                                    T              ⁡                              (                f                )                                      ⁢                          b              ⁡                              (                f                )                                                                        (        5        )            where mp(f), x(f), T(f) and m(f) are the Fourier transforms respectively of mp(t), x(t), T(t) and m(t).
Thus, at output of the digitization chains, the direction vector of the source p at the frequency f is no longer ap but T(f) ap. This vector becomes proportional to ap only if the matrix T(f) is proportional to the identity, i.e. if the impulse responses Tn(t) are identical for all n. In practice, the chains are different and the vectors T(f) ap and ap are not collinear. This causes impairment of the performance of the techniques making explicit use of the relationships (1) and (2) such as goniometry techniques.
C—Equalization of the Reception Chains
In order to overcome the limitations described here above, one of the known methods of the prior art consists of the conversion of the observations x(t) into observations z(t). This is done by invariant linear filtering H(t), where H(t) is the diagonal matrix of the impulse responses hn(t). These observations z(t) are given by:z(t)=H(t)*x(t)=H(t)*T(t)*A m(t)+H(t)*T(t) b(t)  (6)the Fourier transform of which is:z(f)=H(f) x(f)=H(f) T(f) A m(f)+H(f) T(f) b(f)  (7)such that H(f) T(f) becomes a matrix proportional to the identity.
The search for H(f) verifying this property corresponds to the operation of equalization of the reception chains. In practice, the method will seek H(f) such that H(f) T(f)=T1(f)I. This consists in choosing the chain 1 as a reference chain and in seeing to it that the other chains resemble the chain 1 as closely as possible after equalization. Thus, the matrix H(f) sought is given by:H(f)=T1(f) T(f)−1  (8)This means that the frequency response, hn(f), of the equalizing filter of the channel n can be written as follows:hn(f)=T1(f)/Tn(f)  (9)
FIG. 3 shows a classically used generic calibration system formed by:                A calibration signal ST(t) corresponding to a sine wave whose frequency is scanned throughout the digitized band Fe in steps of Δf (FIG. 4).        A system 10 for the injection of the calibration signal into the reception-digitization chains 11. Classically, a switch-based system is used. It may be recalled that a reception-digitization chain consists of a certain number of analog elements, referenced by the block ANA in the figure. These are elements such as a frequency transposition chain (based on a synthesizer and local oscillators), one or more power amplifiers, bandpass filters, an anti-aliasing filter, as well as an analog-digital converter (ADC) converting the analog signals into digital signals ready to be processed by a processor. In an antenna processing system, the ADCs of the different channels are synchronized and the totality of these ADCs constitutes the multi-channel sampling system 12. As these different elements are known to those skilled in the art, they shall not be described in detail in the present application.        a system 13 for the processing of the sampled outputs, aimed at estimating the quantities Tn(fi), 1≦i≦M, and deducing therefrom the responses hn(fi), constituting the frequency correction templates.        
The calibration signal shown in FIG. 4 has an adjustable level. It corresponds, for example, to a sine wave whose frequency is scanned by means of a programmable synthesizer, throughout the digitized band in steps of Δf. The number M of frequency positions in the digitized band Fe is then equal to M=Ent(Fe/Δf) where M=Ent(Fe/Δf)+1 according to the centering of the sampling in frequency.
In practice, if the spectrum of the sine wave is computed from a duration of observation of this sine wave referenced T, a sine(x) curve with a bandwidth of 3 dB and a duration in the range of 1/T takes the place of the spectral line.
The system for the injection of the calibration signal is aimed especially at replacing the output of the sensors by an equal-phase and equal-amplitude calibration signal at the input of the reception chains. The sensor signal is replaced by the calibration signal and this calibration signal is, in practice, substantially identical on the different channels.
FIG. 5 gives a schematic view of a first device known to those skilled in the art using switches 15i. The replacement of the sensor signals by the calibration signal is done as follows: the switch 15i switches over in such a way that the different channels i are linked to the channel 16 which enables the injection of the calibration signal simultaneously on all the inputs 17i. The simultaneous injection is done for each position of the line.
The performance of this switching is related especially to that of the switch in terms of matching and insulation.